the aidpro-crc trial

Conditions: Colo-rectal Cancer

Summary: the aidpro-crc trial aims to improve outcomes for patients undergoing surgery for colorectal cancer by using artificial intelligence (ai) to assist surgeons in risk assessment. the trial will evaluate whether ai can help surgeons better predict the risk of complications and death, leading to improved care, fewer complications, and better use of healthcare resources. in this nationwide, randomized clinical trial, participants will be divided into two groups. one group will have their risk assessed by a surgeon using standard clinical methods, while the other group will have their risk assessed by a surgeon using ai assistance. based on the risk level, patients will receive varying levels of perioperative care. the ai-assisted risk assessment aims to tailor the treatment more precisely to each patient's individual needs, thereby reducing complications, readmissions, and mortality. the trial will be conducted at eight hospitals across denmark, involving patients diagnosed with colorectal cancer who are scheduled for curative surgery. all participants will receive standard treatment according to national guidelines, with the only difference being the method of risk assessment. the primary hypothesis is that ai-assisted risk assessment will lead to better patient outcomes compared to standard surgeon-led assessments. the trial also aims to explore whether this approach can lead to more efficient use of healthcare resources. the study builds on a successful pilot project (aid-surg) that showed promising results in reducing complications, hospital stays, and readmissions. this study is a researcher-initiated, nationwide, randomized clinical trial involving patients diagnosed with colorectal cancer across eight hospitals in denmark. participants will be randomly assigned to one of two groups: ai-assisted risk assessment or standard surgeon-led assessment. the intervention focuses on optimizing perioperative care based on individual risk levels determined by either ai or the surgeon's clinical judgment.

detailed description: the aidpro-crc trial is an investigator-initiated nationwide multicenter randomized controlled trial. the trial aims to investigate the clinical effects of an ai-augmented solution "aidpro manual crc" for optimization of perioperative treatment by personalized risk stratification of patients undergoing crc surgery. the protocol adheres to the spirit statement recommendations. the aidpro-crc trial is a pre-market, pivotal stage, confirmatory clinical investigation designed to evaluate the safety and effectiveness of the aidpro manual crc algorithm. as a pivotal clinical investigation, this study is critical for generating the robust evidence required to support regulatory submissions for ce marking. the investigation involves an interventional approach, meaning that participants will undergo specific procedures or treatments as part of the study. this design has been specifically chosen to rigorously assess the performance of the aidpro manual crc device in a real-world clinical setting, providing the necessary data to demonstrate its safety and effectiveness. the results from this trial will be used to seek ce marking, enabling the aidpro manual crc to be brought to market in the future. all patients referred to a colorectal mdt with clinical suspected or histopathological verified crc will undergo eligibility assessment for inclusion in the study. only patients with confirmed clinical and/or pathological evidence of crc and scheduled for curative crc surgery will be considered eligible for participation in the study. all patients will be treated according to local, national and international guidelines and recommendations by qualified colorectal surgeons. eligible patients with newly diagnosed crc cancer planned for curative intended surgery will be randomly exposed to either an ai-augmentation risk stratification model (intervention arm) or to surgical assessment-based risk stratification model (control arm) for perioperative treatment aiming to optimize their performance leading up to the planned surgery, and thus striving to reduce post-surgery complications. the randomization allocated 1:1 will happen on the individual patient basis, and on a per site level. only the individual colorectal surgeon, who is doing the initial input of data into the randomization software and executes the randomization process gets to know the risk prediction modality of the individual patient. throughout the perioperative optimization phase, it is crucial to highlight that the patient is always blinded to the modality of risk prediction. the surgeon performing the randomization, who is also involved in the assessment and treatment of the patient, may therefore be potentially biased in their evaluation. all other assessors involved in the perioperative treatment are sought to be blinded whenever possible to ensure fair and unbiased treatment for all participating patients in the trial. an independent data manager will provide the data in a blinded format. unblinding to the investigators will occur only after all analyses have been completed and the primary investigator has prepared two versions of the abstract for the primary publication-one in which the results favor the intervention (ai-augmented risk-prediction) group and one in which they favor the control group (expert-based risk-prediction), if a difference between the interventions is identified. study flow and interventions: the aidpro-crc trial investigates the impact of using an ai-augmented risk estimation software device called the "aidpro manual crc" as a decision support tool versus traditional surgeon-based risk stratification for determining the type of perioperative treatment trajectories patients undergoing elective curative-intent surgery for newly diagnosed colorectal cancer (crc) should be offered. this description will cover the nature of the intervention, treatment protocols, and follow-up procedures involved in the trial. study flow: all new patients referred with clinical suspected or histopathological verified crc to one of the participating centers will follow local standards of care including diagnostic evaluation and assessments as defined by the relevant local and national guidelines. this all takes place at the "screening phase". no new assessments or evaluations to the patients are introduced because of the aidpro-crc trial at this phase, and all relevant information to be used at the time of screening will be collected as a part of the standard workflow at each center. the time a patient spends in the screening phase is not influenced by the aidpro-crc trial, and it is the responsibility of each participation center to adhere to the current wait-times in this phase as defined in the relevant danish legislation. at the mdt-conference in the screening phase the decision is made whether the patient is planned for immediate curative intended surgery, or is qualified for neoadjuvant treatment with the potential for later curative intended surgery as per relevant clinical guidelines and assessment. if the patient is scheduled for neoadjuvant treatment, this treatment must take place before any preoperative optimization is performed. the aidpro manual crc device and the randomization module for patient allocation to intervention- and control-arm are hosted within a dedicated application called "mdtplus". this application is designed in collaboration with koncern digitalisering, region sjaelland and is to comply with relevant legislations and rules regarding patient related health data in a cross-regional trial setup. the mdtplus application is designed as a platform to display patient related health information of interest in relation to treatment for crc. the use of the mdtplus application in the aidpro-crc trial is limited to providing a platform for execution of the randomization-sequence and aidpro manual crc device to be used in the trial. patients who have been discussed at the multidisciplinary team (mdt) conference with confirmed crc and hereafter are planned for curative intended surgery, or have the potential for curative intended surgery following relevant neo-adjuvant treatment, will undergo eligibility assessment. when a patient has given informed consent and they are eligible for enrollment in the study, an investigator can execute the randomization process within the mdtplus application. to standardize the procedure for the clinical experts, across the intervention and control arms of the study, the same data needed for the aidpro manual crc device to make a risk-estimation is entered into the mdtplus application for all patients, before the randomization procedure takes place. this data can be populated in the application before the patient is enrolled in the study anytime during the screening phase, but the randomization sequence can only be executed when the patient has met all inclusion criteria and has given informed consent for study participation. if the patient is not enrolled in the study the data in the mdtplus application will be automatically deleted within 90 days, (except data of age and gender to be used for the screening log). this approach, with uniform input of data before the randomization process takes place, ensures that the randomization is then controlled by the application and conducted at the patient level. to ensure that the aidpro manual crc device only makes risk estimations on patients which are enrolled in the study and have provided informed consent, a unique patient consent id code should be entered into the software platform under the section of "patient samtykke id". this unique code is provided enclosed to each informed consent form, and no two codes throughout the study are alike. when entering this code, the enrolling doctors hereby ensures that the patient have provided informed consent and are meeting the inclusion criteria. the patient id code is also used for randomizations sequence, ensuring that each patient is randomized following the previous described randomization sequence, which is built into the backend of the mdtplus application. however, no references to which trail intervention arm can be made from a patient id code. for one group of patients, the application will provide the suggested 1-year risk of mortality and corresponding risk group a, low risk (\ 15% risk of 1-year mortality) group (based on the ai-augmentation model) - constituting the intervention arm (augmented decision process). for the complementary group of patients, the software will only prompt the surgical experts to provide the risk group (a, b c or d) - and this will constitute the control arm. the assigned risk groups will explicitly determine the individualized treatment trajectories each patient is offered, regardless of whether they are in the intervention (ai-augmentation model-based risk stratification) or control arm (expert-based risk stratification). this study design implies that all patients enrolled in the study, regardless of whether the patient is in the intervention- or control arm, gets offered the same perioperative treatment, the only difference are whether the decision of what treatment strata to allocate for the individual patient is made by an ai-augmented risk prediction model or a surcical expert. patients not enrolled in the trial, but still eligible for treatments offered by the respective surgical department will still have the possibility to benefit from the perioperative treatment interventions, as these perioperative treatment interventions are part of standard clinical practice at each participating center, and enrollment in the aidpro-crc trial are not a criteria for receiving the treatment at each participating site. the randomization design will ensure that the allocation of patients to the different trial arms is unbiased and patient-blinded at the time of inclusion, balancing potential inter- and intra- center variation. this approach will further standardize and balance potential variation due to the effect of learning (all colorectal surgeons will learn from the same supervised learning paradigm). after the randomization process and risk stratification procedure the patient will adhere to one of the four defined trajectories in the preoperative phase, named a, b, c, and d; which are all part of standard offered treatment to patients with newly diagnosed crc at each participating site. study flow for patients scheduled for neo-adjuvant treatment if a patient is scheduled for neo-adjuvant treatment, the relevant treatment as per national guidelines and by the mdt-conference decision, must be completed before the perioperative optimization included in the relevant treatment trajectory , to which either an ai-augmented decision (interventions-arm) or surgical expert (control-arm) have allocated can begin. the relevant perioperative treatment should be scheduled to begin either 2 or 4 weeks before the possible date of surgery, depending on which treatment trajectory the patient is allocated to. if a patient which have received neoadjuvant treatment no longer have indication for curative intended surgery at the response evaluation they will be excluded from the study as pr. exclusion criteria's. the study design implies that patients are to be stratified into relevant treatment trajectories before any neo-adjuvant treatment has taken place immediately after the first initial mdt-conference and must complete the neo-adjuvant treatment before any perioperative optimization can take place. intervention: ai-augmented vs. surgeon-based risk stratification: in the aidpro-crc trial, the sole intervention is the method of risk stratification (ai-augmented vs. surgeon-based) used to determine perioperative treatment plans. all other aspects of treatment, including the specific perioperative care bundles, follow established national guidelines and are consistent across all trial sites and interventions-arms, and are described in appendix 1. the trial's objective is to assess whether ai-augmented risk stratification can reduce postoperative complications and improve outcomes compared to traditional clinical assessments by surgical experts. there are two arms in the trial: * ai-augmented risk stratification (intervention arm) * description: a state-of-the-art artificial intelligence (ai) model called aidpro manual crc is used as a decision support tool to estimate the 1-year mortality risk for each patient. * purpose: the ai model uses various patient data points to predict the risk and tailor the perioperative treatment bundle accordingly. * expected outcome: by leveraging ai, the trial aims to enhance the precision of risk stratification, potentially reducing postoperative complications and improving patient outcomes. * surgeon-based risk stratification (control arm) * description: experienced surgical experts perform the risk stratification based on their clinical judgment and existing guidelines. * purpose: traditional clinical assessments are used to categorize patients into appropriate risk groups and assign perioperative treatment bundles. * expected outcome: this arm serves as the control to evaluate the effectiveness of the ai model against standard clinical practices. ai-augmented risk stratification algorithm: the aidpro manual crc is an in-house medical device (class iia) designed for clinical decision support for colorectal surgeons managing the perioperative care planning of patients undergoing curative intended elective surgery for colorectal cancer. the description of the aidpro manual crc adheres to the spirit-ai extension guidelines. the aidpro manual crc device is developed to be used by specialized colorectal surgeons or colorectal surgeons in training during multidisciplinary team conferences and the subsequent surgical consultation at the participating colorectal centers in the aidpro-crc trial as the specific needs of target patient groups cannot be met, or cannot be met at the appropriate level of performance, by an equivalent ce-marked device available on the market. the intention is to obtain a ce-mark for the aidpro manual crc device. the aidpro manual crc is version 1.0.0 "v1" of a prediction model designed for clinical decision support developed and manufactured by center for surgical sciences (css) and builds upon learnings from the "ais-1" model used in the aid-surg pilot study. the tool's core is a prediction model trained on a national cohort in denmark. the device takes risk factors as input and outputs an estimation of the risk of dying within one year following surgery, and a suggestion for risk strata (a, b, c or d) based on the predicted risk. colorectal surgeons in multidisciplinary teams or following preoperative clinical follow-up may use the tool's suggested risk strata for decision support to choose between evidence based perioperative treatment trajectories. the device is only intended to provide clinical decision support to colorectal surgeons at multidisciplinary team conferences and the subsequent surgical consultation and physical examination in relation to adult patients (age \> 18 years) undergoing elective curative intended surgery for colorectal cancer at the participating colorectal centers in the aidpro-crc trial. the device is developed to be used by specialized colorectal surgeons (doctors of medicine/"m.d.'s") or colorectal surgeons in-training at the participating centers. the surgeons should be proficient/native in danish and english. the device is not intended to be used for deciding if surgery should be performed, for non-curative intended surgery, for non-elective surgical procedures, or in the surgical treatment of all children or adults with other oncological diagnoses than colorectal cancer. the device is not intended to be used in acute or emergency care settings. the device is not intended to be used outside the participating colorectal centers in the aidpro-crc trial at this time. algorithm development and covariate selection the dataset upon which the model is build, and trained are consisting of data sources from the danish colorectal cancer group (dccg) database (dccg.dk)covering all patients with newly diagnosed crc in denmark, the danish national patient registry (dnpr), the danish register of laboratory results for research database (rlrr)and the medicinal products statistics database (med). these data sources were all transformed into the observational medicine outcomes network common data model (omop-cdm) and merged, utilizing open-source tools from the observational health data science and informatics (ohdsi) community. data was collected with the initial index date set to the date of surgery and a one-year time-at-risk. for data quality, omop-cdm ensured mandatory fields like sex and age were complete. in the danish colorectal cancer group (dccg) data, mutually exclusive covariates were separated from missing values. in other sources, distinguishing between absent and missing records was challenging due to the nature of electronic health record (ehr) data. categorical variables were one-hot encoded, interpreting missing and negative values as zero, while missing continuous values were treated as na and excluded. approximately 15.000 different variables in the harmonized omop-database were addressed using both a data-driven and clinical expert-based approach. using a supervised feature selection process utilizing six different modalities of problem-based feature selection the initial 15.000 variables were narrowed down to approximately 100 clinical variables were selected for the basis of the aidpro-algorithm. based on a criterion of availability for manual input by the surgeon this selection of variables was further narrowed down to 40 variables. a selection of 12(excluding patient id-number, cpr) variables were then identified for use in the aidpro-algorithm for risk assessment using both a data-driven and clinical expert-based approach. model performance was assessed using the area under the receiver operating characteristic curve (auroc) and the area under the precision-recall curve (auprc). calibration was evaluated using calibration intercept, slope, and calibration-in-the-large, with visual representations of receiver operating characteristic curves, precision-recall curves, and calibration plots. this approach ensured a systematic procedure for data acquisition and selection, robust handling of poor-quality data, and effective regression analysis with sophisticated covariate selection tools. the surgeons are to access the aidpro manual crc device via a secure connection provided by the mdtplus application. the manufacturer, in collaboration with the sponsor and principal investigator (pi), will maintain an access log throughout the study, ensuring that only authorized and qualified personnel can access the algorithm. if the study is terminated for any reason, access to the software will be remotely disabled for all investigators. the surgeon will manually input the required input-data into the aidpro manual crc device. the covariates needed for aidpro manual crc device to run should be carefully retrieved from the patient's medical records or clinically assed. it is crucial that the information for each variable that is used by the model is retrieved and inputted according to the following description, to guarantee the closest correspondence between the real-life patient data and the data that were used to train and test the model. output of aidpro manual crc device: the output of the prediction should be interpreted as an estimation of a single patient's risk of mortality within 1 year following elective curative intended surgery, given the entered covariates. based on the estimated 1-year risk of mortality, a corresponding risk stratum is suggested (a (\ 15%)). the output should only be considered as decision support to choose the type of perioperative care offered to the patient, however, ultimately the decision will rely on the medical doctor treating the patient. it should be noted, that the individual risk estimated is based on a model describing a historical national cohort, that several risk factors and covariate relationships might not be included in the model, and that the covariates contributions' to the risk or the risk estimate can not be interpreted as causal or used in causal inference. if the individual colorectal surgeon finds that the aidpro manual crc device is not in accordance with the surgeon's assessment of the patient's risk prediction, the surgeon always has the option to "overrule" the ai-augmented suggested decision and thereby place the patient in a different risk strata. this must be recorded in the mdtplus application, the patients ehr and subsequently noted in the patient's ecrf. each of the four possible clinical trajectories (strata a-d) in which the aidpro manual crc device allocates a patient to in the intervention-arm, includes a number of clinical interventions pre-, intra-, and postoperatively. only qualified colorectal surgeons with extensive experience in assessing patients risk factors will use the aidpro manual crc algorithm in the trial. the surgeons who will use the aidpro manual crc device in the aidpro-crc trial will receive both written training material as well as structured supervised training set in a simulation environment in order to familiarize with the tool, and use it accordingly to the manufactures instructions. a detailed description describing the instructions for use can be found in in the technical documentation regarding the instruction for use of the aidpro manual crc device: expert-based risk stratification: in the expert-based risk stratification arm (control-arm), experienced surgical experts utilize their clinical judgment and adhere to established guidelines to assess and categorize the risk levels of patients undergoing colorectal cancer surgery. after the surgeon have provided the needed variables in the mdtplus platform and have successfully randomized the patient, the surgeon is prompted to categorize the patient into one of the four risk stratification strata a-d and subsequent treatment trajectories, based upon their expected 1-year risk of mortality (a (\ 15%)). the surgeons are only to categorize the patient in a category and should not make a precise guess of expected risk of 1-year mortality in percent. in the mdtplus application, the patients ehr and in the ecrf it will be registered which surgeon who has provided the risk-stratification. the surgeon is to perform the risk-stratification based on clinical presentation and expert-based analysis and evaluation of various patient factors, including medical history, physical examination findings, diagnostic test results, any relevant comorbidities of the clinical presentation of the patient, taking into account previous health record and inputs made at the mdt-conference. the surgeon-based risk-estimation should not be limited to the covariates the aidpro-algorithm has available for risk-stratification in the intervention-arm, and should thus reflect an average colorectal expert based assessment of the patients 1-year estimated risk of mortality. after the surgeon has provided the risk-stratification for patients in the control-arm, the patients will receive the same perioperative treatment as patients placed in similar risk-stratification-categories by the intervention-arm. perioperative treatment bundles perioperative treatment bundles to which either patients in the interventions arm of the study (ai-augmentation model-based risk stratification) or patients in the control arm of the study (expert-based risk stratification) are assigned to, are not unique to the study. the clinical interventions are evidence-based and adhere to danish and international guidelines in colorectal surgery. interventions, individually and collectively aim to counteract the perioperative risk to improve postoperative outcome. the exact distribution of the interventions between the clinical trajectories is based on the risk profile of patients in each trajectory. when the patient is allocated, the perioperative course is planned by the coordinating nurse at each participating center. follow-up period: * duration: each patient will be monitored for a minimum of one year post-surgery. * activities: follow-up includes regular clinical assessments during the post-operative stay at the colorectal ward, monitoring for postoperative complications, and collection of data on outcomes such as mortality, morbidity, and quality of life. * objective: to evaluate the long-term effects of the perioperative treatment strategies and compare the outcomes between the ai-augmented and surgeon-based risk stratification groups.

Eligibility criteria:
inclusion criteria: to be eligible for inclusion, the patient must: * have histologically confirmed clinical and/or pathological evidence of suspected first occurrence of clinical stage i to iv colorectal carcinoma. * be ≥ 18 years of age on the date of signing the informed consent. * provide written informed consent prior to registration according to the local regulatory requirements. * have indication for elective curative intended surgery decided by the mdt-conference * available information about the covariates needed for the aidpro manual crc device to execute not assessed/directly evaluated by the surgeon (e.g. asa-score and who-performance score) o blood test (must not be older than 30 days from the time of inclusion): carcinoembryonic ag(cea) \[mass/volume\] in serum or plasma (microgram per liter) c-reactive protein (crp) \[mass/volume\] in serum or plasma (milligram per liter) hemoglobin (hgb) \[moles/volume\] in blood (millimole per liter) bilirubin \[moles/volume\] in serum or plasma (micromole per liter) albumin \[mass/volume\] in serum or plasma (gram per liter) sodium (na) \[moles/volume\] in serum or plasma (millimole per liter) egfr \[milliliters/minute\] ml/min/1,73 m2 calculated from creatinin and height exclusion criteria: the patient may be excluded from participation in the trial if the patient: * have indication for elective curative intended surgery revised and canceled in the preoperative phase. * has any serious or uncontrolled medical disorder that, in the opinion of the investigator or treating physician, may increase the risk associated with study participation, impair the ability of the subject to receive protocol intervention, or interfere with the interpretation of study results. * has any language and/or mental diasability imparing the ability to actively participate in perioperative interventions and the ability to answer relevant questionaries.

Keywords: prehabilitation ai artificial intelligence risk-estimation perioperative post-operative complications risk-stratification algorithm